Instructions to use Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2")
model = AutoModelForCausalLM.from_pretrained("Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2

SGLang

How to use Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2 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 "Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2 with Docker Model Runner:
```
docker model run hf.co/Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
```

openchat-3.5-0106_Rebased_Mistral-7B-v0.2 / README.md

Pretergeek

Adding Evaluation Results (#1)

012ebea verified about 2 years ago

preview code

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	---
	license: apache-2.0
	model-index:
	- name: openchat-3.5-0106_Rebased_Mistral-7B-v0.2
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: IFEval (0-Shot)
	type: HuggingFaceH4/ifeval
	args:
	num_few_shot: 0
	metrics:
	- type: inst_level_strict_acc and prompt_level_strict_acc
	value: 37.06
	name: strict accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: BBH (3-Shot)
	type: BBH
	args:
	num_few_shot: 3
	metrics:
	- type: acc_norm
	value: 10.91
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MATH Lvl 5 (4-Shot)
	type: hendrycks/competition_math
	args:
	num_few_shot: 4
	metrics:
	- type: exact_match
	value: 3.85
	name: exact match
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GPQA (0-shot)
	type: Idavidrein/gpqa
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 2.91
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MuSR (0-shot)
	type: TAUR-Lab/MuSR
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 20.57
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU-PRO (5-shot)
	type: TIGER-Lab/MMLU-Pro
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 20.33
	name: accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
	name: Open LLM Leaderboard
	---

	This model was created as an experiment on using LoRA extraction to replicate [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) using [Mistral-7B-v0.2](https://huggingface.co/mistral-community/Mistral-7B-v0.2) as a base model instead of the original [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).

	Openchat-3.5-0106 is an excellent model but was based on Mistral-7B-v0.1 which has a context window of 8192 tokens. Mistral-7B-v0.2 has a context window of 32768 tokens. I could have extended OpenChat-3.5 context myself with RoPE and/or YaRN but that has been done. There are many models on HF that have done exactly that. Instead I decided to try and replicate OpenChat-3.5-0106 using the LoRA extraction method available in mergekit. These are the steps I followed:
	- Extract a LoRA with rank 512 from OpenChat-3.5-0106 using [One](https://huggingface.co/imone)'s [Mistral_7B_with_EOT_token](https://huggingface.co/imone/Mistral_7B_with_EOT_token) as the base model.
	- Replicate imone's work by adding the EOT token to Mistral-7B-v0.2, creating [Mistral-7B-v0.2_EOT](https://huggingface.co/Pretergeek/Mistral-7B-v0.2_EOT).
	- Merge the LoRA's weights to the Mistral-7B-v0.2_EOT model.

	This is the result. This model is not meant for use, it was created to test if this method is viable for replacing the base model of fine-tuned models (when tokenizer and weights have not been changed too much). I am uploading here for evaluation. I don't expect this model to match the original OpenChat-3.5-0106 since I used a LoRA with rank 512, so it won't be equivalent to a full fine-tuning. I have been able to extract LoRAs with higher rank, but currently I don't have the resources to merge them with the model as the memory requirements exceed what I have at my disposal.
	If you would like to help my work, check my Ko-Fi and/or Patreon:
	* https://ko-fi.com/pretergeek
	* https://patreon.com/Pretergeek
	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Pretergeek__openchat-3.5-0106_Rebased_Mistral-7B-v0.2)

	\| Metric \|Value\|
	\|-------------------\|----:\|
	\|Avg. \|15.94\|
	\|IFEval (0-Shot) \|37.06\|
	\|BBH (3-Shot) \|10.91\|
	\|MATH Lvl 5 (4-Shot)\| 3.85\|
	\|GPQA (0-shot) \| 2.91\|
	\|MuSR (0-shot) \|20.57\|
	\|MMLU-PRO (5-shot) \|20.33\|