Instructions to use LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2")
model = AutoModelForMultimodalLM.from_pretrained("LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2

SGLang

How to use LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-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 "LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2" \
    --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": "LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2",
		"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 "LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2" \
        --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": "LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2
```

speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2 / README.md

LoneStriker

ExLLaMA V2 quant of speechless-code-mistral-7b-v1.0-5.0bpw-h6-exl2

2e07123 over 2 years ago

preview code

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metadata

language:
  - en
library_name: transformers
pipeline_tag: text-generation
datasets:
  - jondurbin/airoboros-2.2
  - Open-Orca/OpenOrca
  - garage-bAInd/Open-Platypus
  - WizardLM/WizardLM_evol_instruct_V2_196k
  - TokenBender/python_eval_instruct_51k
tags:
  - llama-2
  - code
license: llama2
model-index:
  - name: SpeechlessCoder
    results:
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: 50
            verified: false

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

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

HumanEval

Metric	Value
humaneval-python	50.0

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

Open LLM Leaderboard

Metric	Value
ARC	59.64
HellaSwag	82.25
MMLU	61.33
TruthfulQA	48.45
Average	62.92

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