Instructions to use NovusResearch/Novus-7b-tr_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NovusResearch/Novus-7b-tr_v1 with Transformers:

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

pipe = pipeline("text-generation", model="NovusResearch/Novus-7b-tr_v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NovusResearch/Novus-7b-tr_v1")
model = AutoModelForCausalLM.from_pretrained("NovusResearch/Novus-7b-tr_v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NovusResearch/Novus-7b-tr_v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NovusResearch/Novus-7b-tr_v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NovusResearch/Novus-7b-tr_v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NovusResearch/Novus-7b-tr_v1

SGLang

How to use NovusResearch/Novus-7b-tr_v1 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/Novus-7b-tr_v1" \
    --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/Novus-7b-tr_v1",
		"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/Novus-7b-tr_v1" \
        --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/Novus-7b-tr_v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NovusResearch/Novus-7b-tr_v1 with Docker Model Runner:
```
docker model run hf.co/NovusResearch/Novus-7b-tr_v1
```

Tbh commited on Mar 25, 2024

Commit

40d46f9

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@@ -1,3 +1,111 @@
 ---
 license: apache-2.0
 ---

 ---
+language:
+- en
 license: apache-2.0
+tags:
+- mistral
+- instruct
+- finetune
+- chatml
+- gpt4
+- synthetic data
+- distillation
+base_model: mistralai/Mistral-7B-v0.1
+model-index:
+- name: Thestral-0.1-tr-chat-7B
+  results: []
 ---
+# Thestral-0.1-tr-chat-7B
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/60ca32d2e7bc4b029af088a0/pNId3MzUdSsI20XOM9Dsv.png)
+This model is a full fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on diverse Turkish datasets.
+The model is fully finetuned on translated datasets using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl). These datasets primarily consist of translated versions sourced from [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) and the [Open-Orca/SlimOrca datasets](https://huggingface.co/datasets/Open-Orca/SlimOrca).
+<details><summary>See axolotl config</summary>
+axolotl version: `0.4.0`
+```yaml
+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|>"
+```
+</details><br>