Instructions to use jth01/Arcee-Blitz-6.5bpw-h8-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jth01/Arcee-Blitz-6.5bpw-h8-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="jth01/Arcee-Blitz-6.5bpw-h8-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jth01/Arcee-Blitz-6.5bpw-h8-exl2")
model = AutoModelForCausalLM.from_pretrained("jth01/Arcee-Blitz-6.5bpw-h8-exl2")
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 jth01/Arcee-Blitz-6.5bpw-h8-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jth01/Arcee-Blitz-6.5bpw-h8-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jth01/Arcee-Blitz-6.5bpw-h8-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jth01/Arcee-Blitz-6.5bpw-h8-exl2

SGLang

How to use jth01/Arcee-Blitz-6.5bpw-h8-exl2 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 "jth01/Arcee-Blitz-6.5bpw-h8-exl2" \
    --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": "jth01/Arcee-Blitz-6.5bpw-h8-exl2",
		"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 "jth01/Arcee-Blitz-6.5bpw-h8-exl2" \
        --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": "jth01/Arcee-Blitz-6.5bpw-h8-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jth01/Arcee-Blitz-6.5bpw-h8-exl2 with Docker Model Runner:
```
docker model run hf.co/jth01/Arcee-Blitz-6.5bpw-h8-exl2
```

Configuration Parsing Warning:In config.json: "quantization_config.bits" must be an integer

Arcee-Blitz (24B) is a new Mistral-based 24B model distilled from DeepSeek, designed to be both fast and efficient. We view it as a practical “workhorse” model that can tackle a range of tasks without the overhead of larger architectures.

Quantizations

Coming soon

Model Details

Architecture Base: Mistral-Small-24B-Instruct-2501
Parameter Count: 24B
Distillation Data:
- Merged Virtuoso pipeline with Mistral architecture, hotstarting the training with over 3B tokens of pretraining distillation from DeepSeek-V3 logits
Fine-Tuning and Post-Training:
- After capturing core logits, we performed additional fine-tuning and distillation steps to enhance overall performance.
License: Apache-2.0

Improving World Knowledge

Arcee-Blitz shows large improvements to performance on MMLU-Pro versus the original Mistral-Small-3, reflecting a dramatic increase in world knowledge.

Data contamination checking

We carefully examined our training data and pipeline to avoid contamination. While we’re confident in the validity of these gains, we remain open to further community validation and testing (one of the key reasons we release these models as open-source).

Benchmark Comparison

Benchmark	mistral‑small‑3	arcee‑blitz
MixEval	81.6%	85.1%
GPQADiamond	42.4%	43.1%
BigCodeBench Complete	44.4%	45.5%
BigCodeBench Instruct	34.7%	35.9%
BigCodeBench Complete-hard	16.2%	19.6%
BigCodeBench Instruct-hard	15.5%	15.5%
IFEval	77.44	80.60
BBH	64.46	65.00
GPQA	33.90	36.70
MMLU Pro	44.70	60.20
MuSR	40.90	50.00
Math Level 5	12.00	38.60

Limitations

Context Length: 32k Tokens (may vary depending on the final tokenizer settings and system resources).
Knowledge Cut-off: Training data may not reflect the latest events or developments beyond June 2024.

Ethical Considerations

Content Generation Risks: Like any language model, Arcee-Blitz can generate potentially harmful or biased content if prompted in certain ways.

License

Arcee-Blitz (24B) is released under the Apache-2.0 License. You are free to use, modify, and distribute this model in both commercial and non-commercial applications, subject to the terms and conditions of the license.

If you have questions or would like to share your experiences using Arcee-Blitz (24B), please connect with us on social media. We’re excited to see what you build—and how this model helps you innovate!

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Model tree for jth01/Arcee-Blitz-6.5bpw-h8-exl2

Base model

mistralai/Mistral-Small-24B-Base-2501

Finetuned

mistralai/Mistral-Small-24B-Instruct-2501

Finetuned

arcee-ai/Arcee-Blitz

Quantized

(21)

this model