Instructions to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Use Docker

docker model run hf.co/eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

LM Studio
Jan

vLLM

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Ollama
How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with Ollama:
```
ollama run hf.co/eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
```
Unsloth Desktop

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with Docker Model Runner:
```
docker model run hf.co/eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M
```

Lemonade

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Run and chat with the model

lemonade run user.edgeai-docs-qwen2.5-coder-0.5b-lora-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "eoinedge/edgeai-docs-qwen2.5-coder-0.5b-lora:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

eoinedge commited on May 12

Commit

7ad688e

verified ·

1 Parent(s): 338bb27

Push trained edgeai-0.5b adapter

Browse files

Files changed (19) hide show

.gitattributes +2 -0
README.md +166 -125
adapter_config.json +7 -5
adapter_model.safetensors +2 -2
chat_template.jinja +54 -6
checkpoint-1944/README.md +207 -0
checkpoint-1944/adapter_config.json +43 -0
checkpoint-1944/adapter_model.safetensors +3 -0
checkpoint-1944/chat_template.jinja +54 -0
checkpoint-1944/optimizer.pt +3 -0
checkpoint-1944/rng_state.pth +3 -0
checkpoint-1944/scheduler.pt +3 -0
checkpoint-1944/tokenizer.json +3 -0
checkpoint-1944/tokenizer_config.json +30 -0
checkpoint-1944/trainer_state.json +1392 -0
checkpoint-1944/training_args.bin +3 -0
tokenizer.json +3 -0
tokenizer_config.json +30 -0
training_args.bin +2 -2

.gitattributes CHANGED Viewed

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+checkpoint-1944/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md CHANGED Viewed

@@ -1,166 +1,207 @@
 ---
-license: apache-2.0
-base_model: Qwen/Qwen1.5-0.5B
 library_name: peft
-language:
-  - en
-tags:
-  - lora
-  - peft
-  - qwen
-  - edge-ai
-  - edge-impulse
-  - documentation
-  - code-generation
-  - conversational
 pipeline_tag: text-generation
 ---
-# edgeai-docs-embedding-qwen1.5-0.5b-instruct
-A LoRA adapter fine-tuned on **1,794 Edge Impulse / Edge AI MDX documentation files** from the [Edge Impulse documentation](https://docs.edgeimpulse.com), built on top of [`Qwen/Qwen1.5-0.5B`](https://huggingface.co/Qwen/Qwen1.5-0.5B).
-Optimized for:
-- Answering developer questions about Edge Impulse Studio, SDK, and APIs
-- Summarizing technical documentation
-- Generating code snippets for edge ML workflows
-- Lightweight local/edge deployment
-> **Larger variants in training:** [1.5B](https://huggingface.co/eoinedge/edgeai-qwen2.5coder-1.5b-lora) · [7B](https://huggingface.co/eoinedge/edgeai-qwen2.5coder-7b-lora) (Qwen2.5-Coder base)
----
 ## Model Details
-| Property | Value |
-|---|---|
-| Base model | `Qwen/Qwen1.5-0.5B` |
-| Adapter type | LoRA (PEFT) |
-| LoRA rank (`r`) | 8 |
-| LoRA alpha | 32 |
-| Target modules | `q_proj`, `v_proj` |
-| LoRA dropout | 0.05 |
-| Trainable parameters | ~786K (0.17% of base) |
-| Training epochs | 3 |
-| Batch size | 4 (× grad accum 2 = effective 8) |
-| Learning rate | 3e-4 (cosine decay) |
-| Sequence length | 512 tokens |
-| Training hardware | Apple M1 Pro (MPS, fp16) |
-| Precision | float16 |
----
-## Training Data
-| Stat | Value |
-|---|---|
-| Source | [Edge Impulse Mintlify documentation](https://docs.edgeimpulse.com) |
-| File format | MDX (Markdown + JSX components) |
-| Total files | 1,794 `.mdx` files |
-| Preprocessing | Frontmatter, JSX tags, imports stripped; code fences unwrapped; links flattened |
-| Chunks generated | ~3,500 × 512-token chunks |
-Topics covered: Studio projects, datasets, DSP blocks, learning blocks, deployment targets, Python SDK, REST API, CLI tools, edge inference, model optimization, and more.
----
-## Usage
-### Load and chat
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-BASE_MODEL = "Qwen/Qwen1.5-0.5B"
-ADAPTER    = "eoinedge/edgeai-docs-embedding-qwen1.5-0.5b-instruct"
-device = "cuda" if torch.cuda.is_available() else \
-         "mps"  if torch.backends.mps.is_available() else "cpu"
-tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
-base  = AutoModelForCausalLM.from_pretrained(BASE_MODEL,
-            dtype=torch.float16 if device != "cpu" else torch.float32,
-            device_map=device)
-model = PeftModel.from_pretrained(base, ADAPTER)
-model.eval()
-```
-### Single question
-```python
-prompt = "How do I collect sensor data using the Edge Impulse data forwarder?"
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-with torch.no_grad():
-    out = model.generate(**inputs, max_new_tokens=512,
-                         do_sample=True, temperature=0.7, top_p=0.95,
-                         pad_token_id=tokenizer.eos_token_id)
-print(tokenizer.decode(out[0], skip_special_tokens=True))
-```
-### Chat template (multi-turn)
-```python
-messages = [
-    {"role": "user", "content": "What is an impulse in Edge Impulse?"}
-]
-formatted = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-inputs = tokenizer(formatted, return_tensors="pt").to(model.device)
-with torch.no_grad():
-    out = model.generate(**inputs, max_new_tokens=512,
-                         do_sample=True, temperature=0.7,
-                         pad_token_id=tokenizer.eos_token_id)
-print(tokenizer.decode(out[0], skip_special_tokens=True))
-```
----
-## Example Prompts
-| Task | Prompt |
-|---|---|
-| Concept explanation | `"What is a DSP block in Edge Impulse?"` |
-| API usage | `"How do I use the Edge Impulse Python SDK to upload data?"` |
-| Deployment | `"How do I deploy a model to an Arduino Nano 33 BLE Sense?"` |
-| Code generation | `"Write Python code to collect IMU data and upload it to Edge Impulse."` |
-| Troubleshooting | `"Why is my Edge Impulse model showing high latency on the Cortex-M4?"` |
----
-## Limitations
-- Based on a 0.5B parameter base model — may struggle with complex multi-step reasoning
-- Training data covers documentation as of mid-2026; newer APIs may not be represented
-- May hallucinate undocumented Edge Impulse features
-- Not suitable for safety-critical or production decision-making systems
-- Validate generated code before running on real hardware
----
-## Related Models
-| Model | Base | Status |
-|---|---|---|
-| This model | Qwen1.5-0.5B | ✅ Available |
-| [eoinedge/edgeai-qwen2.5coder-1.5b-lora](https://huggingface.co/eoinedge/edgeai-qwen2.5coder-1.5b-lora) | Qwen2.5-Coder-1.5B-Instruct | 🔄 Training |
-| [eoinedge/edgeai-qwen2.5coder-7b-lora](https://huggingface.co/eoinedge/edgeai-qwen2.5coder-7b-lora) | Qwen2.5-Coder-7B-Instruct | 🔄 Training |
-| [eoinedge/arduino-qwen0.5-lora](https://huggingface.co/eoinedge/arduino-qwen0.5-lora) | Qwen1.5-0.5B | ✅ Available (Arduino docs) |
----
-## Citation
-```bibtex
-@misc{edgeai-docs-embedding-qwen1.5-0.5b-instruct,
-  author       = {Jordan, Eoin},
-  title        = {edgeai-docs-embedding-qwen1.5-0.5b-instruct},
-  year         = {2026},
-  publisher    = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/eoinedge/edgeai-docs-embedding-qwen1.5-0.5b-instruct}}
-}
-```

 ---
+base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
 library_name: peft
 pipeline_tag: text-generation
+tags:
+- base_model:adapter:Qwen/Qwen2.5-Coder-0.5B-Instruct
+- lora
+- transformers
 ---
+# Model Card for Model ID
+<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
+### Model Description
+<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources [optional]
+<!-- Provide the basic links for the model. -->
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+## Uses
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Direct Use
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+[More Information Needed]
+### Downstream Use [optional]
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+[More Information Needed]
+### Out-of-Scope Use
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+[More Information Needed]
+## Bias, Risks, and Limitations
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+[More Information Needed]
+### Recommendations
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## How to Get Started with the Model
+Use the code below to get started with the model.
+[More Information Needed]
+## Training Details
+### Training Data
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+[More Information Needed]
+### Training Procedure
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Preprocessing [optional]
+[More Information Needed]
+#### Training Hyperparameters
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+#### Speeds, Sizes, Times [optional]
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+[More Information Needed]
+## Evaluation
+<!-- This section describes the evaluation protocols and provides the results. -->
+### Testing Data, Factors & Metrics
+#### Testing Data
+<!-- This should link to a Dataset Card if possible. -->
+[More Information Needed]
+#### Factors
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+[More Information Needed]
+#### Metrics
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+[More Information Needed]
+### Results
+[More Information Needed]
+#### Summary
+## Model Examination [optional]
+<!-- Relevant interpretability work for the model goes here -->
+[More Information Needed]
+## Environmental Impact
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+## Technical Specifications [optional]
+### Model Architecture and Objective
+[More Information Needed]
+### Compute Infrastructure
+[More Information Needed]
+#### Hardware
+[More Information Needed]
+#### Software
+[More Information Needed]
+## Citation [optional]
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+**BibTeX:**
+[More Information Needed]
+**APA:**
+[More Information Needed]
+## Glossary [optional]
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+[More Information Needed]
+## More Information [optional]
+[More Information Needed]
+## Model Card Authors [optional]
+[More Information Needed]
+## Model Card Contact
+[More Information Needed]
+### Framework versions
+- PEFT 0.19.1

adapter_config.json CHANGED Viewed

@@ -3,7 +3,7 @@
   "alpha_pattern": {},
   "arrow_config": null,
   "auto_mapping": null,
-  "base_model_name_or_path": "Qwen/Qwen1.5-0.5B",
   "bias": "none",
   "corda_config": null,
   "ensure_weight_tying": false,
@@ -16,25 +16,27 @@
   "layers_pattern": null,
   "layers_to_transform": null,
   "loftq_config": {},
-  "lora_alpha": 16,
   "lora_bias": false,
   "lora_dropout": 0.05,
   "megatron_config": null,
   "megatron_core": "megatron.core",
   "modules_to_save": null,
   "peft_type": "LORA",
-  "peft_version": "0.18.1",
   "qalora_group_size": 16,
   "r": 8,
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "v_proj",
-    "q_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",
   "trainable_token_indices": null,
   "use_dora": false,
   "use_qalora": false,
   "use_rslora": false

   "alpha_pattern": {},
   "arrow_config": null,
   "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-Coder-0.5B-Instruct",
   "bias": "none",
   "corda_config": null,
   "ensure_weight_tying": false,
   "layers_pattern": null,
   "layers_to_transform": null,
   "loftq_config": {},
+  "lora_alpha": 32,
   "lora_bias": false,
   "lora_dropout": 0.05,
+  "lora_ga_config": null,
   "megatron_config": null,
   "megatron_core": "megatron.core",
   "modules_to_save": null,
   "peft_type": "LORA",
+  "peft_version": "0.19.1",
   "qalora_group_size": 16,
   "r": 8,
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
+    "q_proj",
+    "v_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",
   "trainable_token_indices": null,
+  "use_bdlora": null,
   "use_dora": false,
   "use_qalora": false,
   "use_rslora": false

adapter_model.safetensors CHANGED Viewed

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chat_template.jinja CHANGED Viewed

@@ -1,6 +1,54 @@
-{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
-You are a helpful assistant<|im_end|>
-' }}{% endif %}{{'<|im_start|>' + message['role'] + '
-' + message['content'] + '<|im_end|>' + '
-'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
-' }}{% endif %}

+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

checkpoint-1944/README.md ADDED Viewed

	@@ -0,0 +1,207 @@

+---
+base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:Qwen/Qwen2.5-Coder-0.5B-Instruct
+- lora
+- transformers
+---
+# Model Card for Model ID
+<!-- Provide a quick summary of what the model is/does. -->
+## Model Details
+### Model Description
+<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources [optional]
+<!-- Provide the basic links for the model. -->
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+## Uses
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Direct Use
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+[More Information Needed]
+### Downstream Use [optional]
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+[More Information Needed]
+### Out-of-Scope Use
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+[More Information Needed]
+## Bias, Risks, and Limitations
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+[More Information Needed]
+### Recommendations
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## How to Get Started with the Model
+Use the code below to get started with the model.
+[More Information Needed]
+## Training Details
+### Training Data
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+[More Information Needed]
+### Training Procedure
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Preprocessing [optional]
+[More Information Needed]
+#### Training Hyperparameters
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+#### Speeds, Sizes, Times [optional]
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+[More Information Needed]
+## Evaluation
+<!-- This section describes the evaluation protocols and provides the results. -->
+### Testing Data, Factors & Metrics
+#### Testing Data
+<!-- This should link to a Dataset Card if possible. -->
+[More Information Needed]
+#### Factors
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+[More Information Needed]
+#### Metrics
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+[More Information Needed]
+### Results
+[More Information Needed]
+#### Summary
+## Model Examination [optional]
+<!-- Relevant interpretability work for the model goes here -->
+[More Information Needed]
+## Environmental Impact
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+## Technical Specifications [optional]
+### Model Architecture and Objective
+[More Information Needed]
+### Compute Infrastructure
+[More Information Needed]
+#### Hardware
+[More Information Needed]
+#### Software
+[More Information Needed]
+## Citation [optional]
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+**BibTeX:**
+[More Information Needed]
+**APA:**
+[More Information Needed]
+## Glossary [optional]
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+[More Information Needed]
+## More Information [optional]
+[More Information Needed]
+## Model Card Authors [optional]
+[More Information Needed]
+## Model Card Contact
+[More Information Needed]
+### Framework versions
+- PEFT 0.19.1

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+{
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+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-Coder-0.5B-Instruct",
+  "bias": "none",
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+  "eva_config": null,
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+  "use_rslora": false
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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

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