Instructions to use uukuguy/speechless-code-mistral-7b-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use uukuguy/speechless-code-mistral-7b-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="uukuguy/speechless-code-mistral-7b-v1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("uukuguy/speechless-code-mistral-7b-v1.0")
model = AutoModelForCausalLM.from_pretrained("uukuguy/speechless-code-mistral-7b-v1.0")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use uukuguy/speechless-code-mistral-7b-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "uukuguy/speechless-code-mistral-7b-v1.0"
# 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-code-mistral-7b-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/uukuguy/speechless-code-mistral-7b-v1.0

SGLang

How to use uukuguy/speechless-code-mistral-7b-v1.0 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-code-mistral-7b-v1.0" \
    --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-code-mistral-7b-v1.0",
		"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-code-mistral-7b-v1.0" \
        --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-code-mistral-7b-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use uukuguy/speechless-code-mistral-7b-v1.0 with Docker Model Runner:
```
docker model run hf.co/uukuguy/speechless-code-mistral-7b-v1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

speechless-code-mistral-7b-v1.0

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

Use the following dataset to fine-tune mistralai/Mistral-7B-v0.1 in order to improve the model's reasoning and planning abilities.

Total 201,981 samples.

jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples.
Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples.
garage-bAInd/Open-Platypus: 100%, 24,926 samples.
WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples
TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples
Spider: 8,659 samples

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	51.21951219512195

Big Code Evaluation

	Humaneval	Java	Javascript	CPP	Php	Rust	Swift	R	Lua	D	Racket	Julia
pass@1	0.4260	0.3165	0.4241	0.3467	0.3548	0.2454	0.0000	0.1735	0.2942	0.1087	0.0000	0.3081
pass@10	0.5784	0.4506	0.5891	0.4845	0.4997	0.3858	0.0000	0.2516	0.4126	0.2018	0.0000	0.4427

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

lm-evaluation-harness

{'ARC (acc_norm)': 0.6109215017064846,
'HellaSwag (acc_norm)': 0.8358892650866361,
'MMLU (acc)': 0.6325456394049195,
'TruthfulQA (mc2)': 0.4746745250371087,
'Winoground (acc)': 0.7829518547750592,
'GSM8K (acc)': 0.467778620166793,
'DROP (f1)': 0.49585675335570545,
'Open LLM Score': 0.61437428571428571}

Open LLM Leaderboard

Metric	Value
ARC	60.58
HellaSwag	83.47
MMLU	62.98
TruthfulQA	47.9
Winoground	78.69
GSM8K	19.18
Average	58.85

Parameters


lr	2e-4
lr_scheduler_type	cosine
weight_decay	0.0
optim	paged_adamw_8bit
flash_attention	True
rerope	False
max_new_tokens	4096
num_train_epochs	2
bits	4
lora_r	64
lora_alpha	16
lora_dropout	0.05
double_quant	True
quant_type	nf4
dataset_format	airoboros
mini_batch_size	2
grandient_accumulation_steps	32
bf16	True

A40-48G x 2


epoch	2.0
etrain_loss	0.5
etrain_runtime	1 day, 10:25:26.77
etrain_samples_per_second	3.194
etrain_steps_per_second	0.025
eeval_loss	0.5146
eeval_runtime	0:00:25.04
eeval_samples_per_second	7.985
eeval_steps_per_second

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	53.47
ARC (25-shot)	60.58
HellaSwag (10-shot)	83.75
MMLU (5-shot)	62.98
TruthfulQA (0-shot)	47.9
Winogrande (5-shot)	78.69
GSM8K (5-shot)	19.18
DROP (3-shot)	21.19

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Collection

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Evaluation results

pass@1 on HumanEval
self-reported

51.220