Instructions to use blockblockblock/Code-Mistral-7B-bpw5.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use blockblockblock/Code-Mistral-7B-bpw5.5 with Transformers:

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

pipe = pipeline("text-generation", model="blockblockblock/Code-Mistral-7B-bpw5.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("blockblockblock/Code-Mistral-7B-bpw5.5")
model = AutoModelForCausalLM.from_pretrained("blockblockblock/Code-Mistral-7B-bpw5.5")
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

vLLM

How to use blockblockblock/Code-Mistral-7B-bpw5.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "blockblockblock/Code-Mistral-7B-bpw5.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "blockblockblock/Code-Mistral-7B-bpw5.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/blockblockblock/Code-Mistral-7B-bpw5.5

SGLang

How to use blockblockblock/Code-Mistral-7B-bpw5.5 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 "blockblockblock/Code-Mistral-7B-bpw5.5" \
    --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": "blockblockblock/Code-Mistral-7B-bpw5.5",
		"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 "blockblockblock/Code-Mistral-7B-bpw5.5" \
        --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": "blockblockblock/Code-Mistral-7B-bpw5.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use blockblockblock/Code-Mistral-7B-bpw5.5 with Docker Model Runner:
```
docker model run hf.co/blockblockblock/Code-Mistral-7B-bpw5.5
```

Code-Mistral-7B-bpw5.5 / README.md

blockblockblock

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	---
	license: apache-2.0
	datasets:
	- ajibawa-2023/Code-290k-ShareGPT
	- m-a-p/Code-Feedback
	- microsoft/orca-math-word-problems-200k
	- teknium/openhermes
	language:
	- en
	tags:
	- code
	- mathematics
	---

	Code-Mistral-7B


	This Model is trained on refined version of my dataset [Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT).
	Besides this it is trained on following datasets:

	[Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback)

	[orca-math-word-problems-200k](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k)

	[Openhermes](https://huggingface.co/datasets/teknium/openhermes)

	The idea was to check how this Model will perform with both Code & Maths datasets. This model is very good with Coding.
	Maths is still hit & miss but you can test out this model.

	This Model is trained on massive datasets so the results are very good.
	I have used ChatML prompt format.

	Kindly note this is qLoRA version, a rare exception.


	Training:
	Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took almost 33 Hours. Axolotl codebase was used for training purpose.
	Entire data is trained on Mistral.

	Example Prompt:
	This model uses ChatML prompt format.

	```
	<\|im_start\|>system
	You are a helpful AI assistant.<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant

	```
	You can modify above Prompt as per your requirement.


	I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.

	Thank you for your love & support.


	Example Output


	C++

	![image/jpeg](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F64aea8ff67511bd3d965697b%2FjcmEZSRX7s7-B_ZybWwwN.jpeg%3C%2Fspan%3E)%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L65">
	Error Resolving

	![image/jpeg](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F64aea8ff67511bd3d965697b%2Fiy89IxjiZXAY4Id-ieLg7.jpeg%3C%2Fspan%3E)%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L69">
	Matrices

	![image/jpeg](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F64aea8ff67511bd3d965697b%2FzFfq9lBA63wQzy0tP3_hd.jpeg%3C%2Fspan%3E)%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L73">
	Machine Learning

	![image/jpeg](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F64aea8ff67511bd3d965697b%2FNv8dCpNxRtJGkOuulKzmn.jpeg%3C%2Fspan%3E)%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L77">