Instructions to use speechlessai/speechless-coding-7b-16k-tora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use speechlessai/speechless-coding-7b-16k-tora with Transformers:

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

pipe = pipeline("text-generation", model="speechlessai/speechless-coding-7b-16k-tora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("speechlessai/speechless-coding-7b-16k-tora")
model = AutoModelForCausalLM.from_pretrained("speechlessai/speechless-coding-7b-16k-tora")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use speechlessai/speechless-coding-7b-16k-tora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "speechlessai/speechless-coding-7b-16k-tora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "speechlessai/speechless-coding-7b-16k-tora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/speechlessai/speechless-coding-7b-16k-tora

SGLang

How to use speechlessai/speechless-coding-7b-16k-tora 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 "speechlessai/speechless-coding-7b-16k-tora" \
    --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": "speechlessai/speechless-coding-7b-16k-tora",
		"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 "speechlessai/speechless-coding-7b-16k-tora" \
        --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": "speechlessai/speechless-coding-7b-16k-tora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use speechlessai/speechless-coding-7b-16k-tora with Docker Model Runner:
```
docker model run hf.co/speechlessai/speechless-coding-7b-16k-tora
```

speechless-coding-7b-16k-tora

Use the following dataset to fine-tune llm_agents/tora-code-7b-v1.0 in order to improve the model's reasoning and planning abilities.

context window length: 16,384 prompt_type = "alpaca" max_tokens > 128 && < 16384

Total 177,333 samples 316 MB

jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 21,923 samples.
Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 62,973 samples.
garage-bAInd/Open-Platypus: 100%, 22,760 samples.
WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,081 samples
TokenBender/python_eval_instruct_51k: “python” in output .39,596 samples

50 samples/T=0.2/MaxTokens=512/Top_P=0.95

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

HumanEval

Metric	Value
humaneval-python	52.44

Big Code Models Leaderboard

CodeLlama-34B-Python: 53.29

CodeLlama-34B-Instruct: 50.79

                    CodeLlama-13B-Instruct: 50.6

                                            CodeLlama-34B: 45.11

                                            CodeLlama-13B-Python: 42.89

                                            CodeLlama-13B: 35.07

MultiPL-E

                                            | Metric | Value |
                                            | --- | --- |
                                            | python | 55.96 |
                                            | java | 37.84 |
                                            | javascript | 46.93 |
                                            | cpp | 37.48 |
                                            | rust | 29.01 |
                                            | go | 28.99 |
                                            |  sh | 12.11 |
                                            | julia | 31.47 |
                                            | typescript | 47.80 |

LMEval

                                            [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
                                            | Metric | Value |
                                            | --- | --- |
                                            | ARC | |
                                            | HellaSwag | |
                                            | MMLU | |
                                            | TruthfulQA |  |
                                            | Average |  |

Parameters

                                            | | |
                                            |------ | ------ |
                                            | lr | 2e-4 |
                                            | lr_scheduler_type | cosine |
                                            | weight_decay | 0.0 |
                                            | optim | paged_adamw_8bit |
                                            | flash_attention | True |
                                            | rerope | False |
                                            | max_new_tokens | 16384 |
                                            | num_train_epochs | 2 |
                                            | bits | 4 |
                                            | lora_r | 64 |
                                            | lora_alpha | 256 |
                                            | lora_dropout | 0.05 |
                                            | double_quant | True |
                                            | quant_type | nf4 |
                                            | dataset_format | sharegpt |
                                            | mini_batch_size | 2 |
                                            | grandient_accumulation_steps | 32 |
                                            | bf16 | True |

                                            A100-40G x 4

Downloads last month: 977

Datasets used to train speechlessai/speechless-coding-7b-16k-tora

Evaluation results

pass@1 on HumanEval
self-reported

52.439