Instructions to use uukuguy/speechless-code-mistral-orca-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-orca-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-orca-7b-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("uukuguy/speechless-code-mistral-orca-7b-v1.0")
model = AutoModelForCausalLM.from_pretrained("uukuguy/speechless-code-mistral-orca-7b-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use uukuguy/speechless-code-mistral-orca-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-orca-7b-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uukuguy/speechless-code-mistral-orca-7b-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use uukuguy/speechless-code-mistral-orca-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-orca-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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uukuguy/speechless-code-mistral-orca-7b-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-orca-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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uukuguy/speechless-code-mistral-orca-7b-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

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

Use the following dataset to fine-tune Open-Orca/Mistral-7B-OpenOrca 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

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

HumanEval

Metric	Value
humaneval-python	47.561

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

A100-40G x 4


epoch	2.0
etrain_loss	0.4708
etrain_runtime	12:12:53.64
etrain_samples_per_second	9.002
etrain_steps_per_second	0.07
eeval_loss	0.4851
eeval_runtime	0:00:10.31
eeval_samples_per_second	19.385
eeval_steps_per_second	4.846

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	55.33
ARC (25-shot)	59.64
HellaSwag (10-shot)	82.25
MMLU (5-shot)	61.33
TruthfulQA (0-shot)	48.45
Winogrande (5-shot)	77.51
GSM8K (5-shot)	8.26
DROP (3-shot)	49.89

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Model tree for uukuguy/speechless-code-mistral-orca-7b-v1.0

Quantizations

1 model

Datasets used to train uukuguy/speechless-code-mistral-orca-7b-v1.0

Collection including uukuguy/speechless-code-mistral-orca-7b-v1.0

Mistral based Models

Collection

5 items • Updated Feb 23, 2024 • 3

Evaluation results

pass@1 on HumanEval
self-reported

47.561