Instructions to use Badgids/Gonzo-Chat-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Badgids/Gonzo-Chat-7B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Badgids/Gonzo-Chat-7B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Badgids/Gonzo-Chat-7B-GGUF")
model = AutoModelForCausalLM.from_pretrained("Badgids/Gonzo-Chat-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Badgids/Gonzo-Chat-7B-GGUF 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 Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Badgids/Gonzo-Chat-7B-GGUF: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 Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Badgids/Gonzo-Chat-7B-GGUF: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 Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Badgids/Gonzo-Chat-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Badgids/Gonzo-Chat-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Badgids/Gonzo-Chat-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M

SGLang

How to use Badgids/Gonzo-Chat-7B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Badgids/Gonzo-Chat-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Badgids/Gonzo-Chat-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Badgids/Gonzo-Chat-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Badgids/Gonzo-Chat-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Badgids/Gonzo-Chat-7B-GGUF with Ollama:
```
ollama run hf.co/Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Badgids/Gonzo-Chat-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
```

Lemonade

How to use Badgids/Gonzo-Chat-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gonzo-Chat-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Badgids commited on Mar 2, 2024

Commit

710a342

verified ·

1 Parent(s): 0cde54c

first!

Browse files

Files changed (4) hide show

.gitattributes +1 -0
Gonzo-Chat-7B-Q4_K_M.gguf +3 -0
README.md +52 -1
config.json +26 -0

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+oid sha256:f6eb3cd62f932af310ef8eea9fb1297f009969d2d772efa90ea0d1a5c7601532
+size 4523063008

README.md CHANGED Viewed

@@ -1,3 +1,54 @@
 ---
-license: apache-2.0
 ---

 ---
+base_model:
+- Nondzu/Mistral-7B-Instruct-v0.2-code-ft
+- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
+- cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
+- eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO
+library_name: transformers
+tags:
+- mergekit
+- merge
 ---
+# Gonzo-Chat-7B
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+## Merge Details
+### Merge Method
+This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO) as a base.
+### Models Merged
+The following models were included in the merge:
+* [Nondzu/Mistral-7B-Instruct-v0.2-code-ft](https://huggingface.co/Nondzu/Mistral-7B-Instruct-v0.2-code-ft)
+* [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO)
+* [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser)
+### Configuration
+The following YAML configuration was used to produce this model:
+```yaml
+models:
+  - model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO
+    # No parameters necessary for base model
+  - model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
+    parameters:
+      density: 0.53
+      weight: 0.4
+  - model:  NousResearch/Nous-Hermes-2-Mistral-7B-DPO
+    parameters:
+      density: 0.53
+      weight: 0.3
+  - model: Nondzu/Mistral-7B-Instruct-v0.2-code-ft
+    parameters:
+      density: 0.53
+      weight: 0.3
+merge_method: dare_ties
+base_model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO
+parameters:
+  int8_mask: true
+dtype: bfloat16
+```

config.json ADDED Viewed

	@@ -0,0 +1,26 @@

+{
+  "_name_or_path": "Gonzo-Chat-7B",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}