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", device_map="auto")

# 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")

Inference
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 28, 2024

Commit

ca34e98

verified ·

1 Parent(s): 40d46f9

Update README.md

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@@ -1,7 +1,7 @@
 ---
 language:
 - en
-license: apache-2.0
 tags:
 - mistral
 - instruct
@@ -10,102 +10,10 @@ tags:
 - 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>

 ---
 language:
 - en
+license: cc-by-nc-4.0
 tags:
 - mistral
 - instruct
 - gpt4
 - synthetic data
 - distillation
+base_model: mlabonne/NeuralDaredevil-7B
 model-index:
+- name: Novus-7b-tr_v1
   results: []
 ---
+# Novus-7b-tr_v1