Instructions to use uukuguy/speechless-starcoder2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use uukuguy/speechless-starcoder2-7b with Transformers:

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

pipe = pipeline("text-generation", model="uukuguy/speechless-starcoder2-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("uukuguy/speechless-starcoder2-7b")
model = AutoModelForCausalLM.from_pretrained("uukuguy/speechless-starcoder2-7b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use uukuguy/speechless-starcoder2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "uukuguy/speechless-starcoder2-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uukuguy/speechless-starcoder2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/uukuguy/speechless-starcoder2-7b

SGLang

How to use uukuguy/speechless-starcoder2-7b 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 "uukuguy/speechless-starcoder2-7b" \
    --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": "uukuguy/speechless-starcoder2-7b",
		"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 "uukuguy/speechless-starcoder2-7b" \
        --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": "uukuguy/speechless-starcoder2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use uukuguy/speechless-starcoder2-7b with Docker Model Runner:
```
docker model run hf.co/uukuguy/speechless-starcoder2-7b
```

speechless-starcoder2-7b / README.md

uukuguy

Init

c97317d about 2 years ago

preview code

raw

history blame contribute delete

1.91 kB

metadata

language:
  - en
library_name: transformers
pipeline_tag: text-generation
datasets:
  - teknium/OpenHermes-2.5
  - TokenBender/python_eval_instruct_51k
  - codefuse-ai/Evol-instruction-66k
tags:
  - code
license: apache-2.0
model-index:
  - name: SpeechlessCoder
    results:
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: 0
            verified: false

speechless-starcoder2-7b

Code: https://github.com/uukuguy/speechless

Use the following dataset to fine-tune bigcode/starcoder2-7b in order to improve the model's reasoning and planning abilities.

Total 986k samples.

teknium/OpenHermes-2.5
TokenBender/python_eval_instruct_51k
Spider
codefuse-ai/Evol-instruction-66k

How to Prompt the Model

This model accepts the Alpaca instruction format.

For example:

You are an intelligent programming assistant.

### Instruction:
Implement a linked list in C++

### Response:

HumanEval

Metric	Value
humaneval-python

lm-evaluation-harness

{'ARC (acc_norm)': ,
'HellaSwag (acc_norm)': ,
'MMLU (acc)': ,
'TruthfulQA (mc2)': ,
'Winoground (acc)': ,
'GSM8K (acc)': ,
'DROP (f1)': ,
'Open LLM Score': }

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.
ARC (25-shot)
HellaSwag (10-shot)
MMLU (5-shot)
TruthfulQA (0-shot)
Winogrande (5-shot)
GSM8K (5-shot)
DROP (3-shot)