Text Generation
Transformers
PyTorch
English
mistral
Eval Results (legacy)
text-generation-inference
Instructions to use abacaj/mistral-7b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abacaj/mistral-7b-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abacaj/mistral-7b-sft")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("abacaj/mistral-7b-sft") model = AutoModelForCausalLM.from_pretrained("abacaj/mistral-7b-sft") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use abacaj/mistral-7b-sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abacaj/mistral-7b-sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abacaj/mistral-7b-sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/abacaj/mistral-7b-sft
- SGLang
How to use abacaj/mistral-7b-sft 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 "abacaj/mistral-7b-sft" \ --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": "abacaj/mistral-7b-sft", "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 "abacaj/mistral-7b-sft" \ --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": "abacaj/mistral-7b-sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use abacaj/mistral-7b-sft with Docker Model Runner:
docker model run hf.co/abacaj/mistral-7b-sft
How to run inference:
import transformers
import torch
def fmt_prompt(prompt: str) -> str:
return f"""[Instructions]:\n{prompt}\n\n[Response]:"""
if __name__ == "__main__":
model_name = "abacaj/mistral-7b-sft"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = (
transformers.AutoModelForCausalLM.from_pretrained(
model_name,
)
.to("cuda:0")
.eval()
)
prompt = "If A is greater than B and B is greater than C does that make A greater than C?"
prompt_input = fmt_prompt(prompt)
inputs = tokenizer(prompt_input, return_tensors="pt").to(model.device)
input_ids_cutoff = inputs.input_ids.size(dim=1)
with torch.no_grad():
generated_ids = model.generate(
**inputs,
use_cache=True,
max_new_tokens=512,
temperature=0.2,
top_p=0.95,
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
completion = tokenizer.decode(
generated_ids[0][input_ids_cutoff:],
skip_special_tokens=True,
)
print(completion)
Evals:
Code to train model: https://github.com/abacaj/train-with-fsdp
- Downloads last month
- 10
Evaluation results
- pass@1 on HumanEvalself-reported54.270
- pass@1 on MBPPself-reported38.000
- pass@1 on MMLUself-reported45.890

