Instructions to use NovusResearch/Thestral-0.1-tr-chat-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NovusResearch/Thestral-0.1-tr-chat-7B with Transformers:

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

pipe = pipeline("text-generation", model="NovusResearch/Thestral-0.1-tr-chat-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NovusResearch/Thestral-0.1-tr-chat-7B")
model = AutoModelForCausalLM.from_pretrained("NovusResearch/Thestral-0.1-tr-chat-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NovusResearch/Thestral-0.1-tr-chat-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NovusResearch/Thestral-0.1-tr-chat-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NovusResearch/Thestral-0.1-tr-chat-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NovusResearch/Thestral-0.1-tr-chat-7B

SGLang

How to use NovusResearch/Thestral-0.1-tr-chat-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 "NovusResearch/Thestral-0.1-tr-chat-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": "NovusResearch/Thestral-0.1-tr-chat-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 "NovusResearch/Thestral-0.1-tr-chat-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": "NovusResearch/Thestral-0.1-tr-chat-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NovusResearch/Thestral-0.1-tr-chat-7B with Docker Model Runner:
```
docker model run hf.co/NovusResearch/Thestral-0.1-tr-chat-7B
```

Thestral-0.1-tr-chat-7B

This model is a full fine-tuned version of mistralai/Mistral-7B-v0.1 on diverse Turkish datasets.

The model is fully finetuned on translated datasets using axolotl. These datasets primarily consist of translated versions sourced from teknium/OpenHermes-2.5 and the Open-Orca/SlimOrca datasets.

See axolotl config

axolotl version: 0.4.0

base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: NovusResearch/OpenHermes-2.5-Translated-TR-sharegpt-style
    type: sharegpt
    conversation: chatml
  - path: data/merged_all.json
    ds_type: json
    type: sharegpt
    conversation: chatml

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

## Use 
wandb_project: full_finetune
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:


warmup_steps: 10
evals_per_epoch: 0
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "<|im_end|>"
  unk_token: "<unk>"
tokens:
  - "<|im_start|>"

🎯 OpenLLMTurkishLeaderboard

Metric	Value
Avg.	36.41
AI2 Reasoning Challenge	27.24
HellaSwag	33.93
MMLU	40.64
TruthfulQA	47.90
Winogrande	50.86
GSM8k	17.91

Downloads last month: 8

Safetensors

Model size

7B params

Tensor type

F32

Model tree for NovusResearch/Thestral-0.1-tr-chat-7B

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1033)

this model