Instructions to use NumbersStation/nsql-llama-2-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NumbersStation/nsql-llama-2-7B with Transformers:

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

pipe = pipeline("text-generation", model="NumbersStation/nsql-llama-2-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-llama-2-7B")
model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-llama-2-7B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NumbersStation/nsql-llama-2-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NumbersStation/nsql-llama-2-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NumbersStation/nsql-llama-2-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NumbersStation/nsql-llama-2-7B

SGLang

How to use NumbersStation/nsql-llama-2-7B 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 "NumbersStation/nsql-llama-2-7B" \
    --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": "NumbersStation/nsql-llama-2-7B",
		"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 "NumbersStation/nsql-llama-2-7B" \
        --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": "NumbersStation/nsql-llama-2-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NumbersStation/nsql-llama-2-7B with Docker Model Runner:
```
docker model run hf.co/NumbersStation/nsql-llama-2-7B
```

Save and load the nsql-llama-2-7B model

#18

by Or111 - opened Feb 6, 2024

Discussion

Or111

Feb 6, 2024

•

edited Feb 6, 2024

Hi,
I am trying to save and load the nsql-llama-2-7B model after I have finetuned him.
I can see that the model is saved but I can not load it.
this is the code:

'''
from transformers import default_data_collator, Trainer, TrainingArguments
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

trainer.save_model("./my_model")
model.save_pretrained("./my_model1")

model2 = AutoModelForCausalLM.from_pretrained("./my_model1", load_in_8bit=True, torch_dtype=torch.bfloat16, device_map='auto')
model3 = AutoModelForCausalLM.from_pretrained("./my_model1")
'''
loading the model with AutoPeftModelForCausalLM caused a crash:
'''
from peft import AutoPeftModelForCausalLM

model = AutoPeftModelForCausalLM.from_pretrained("./my_model1", local_files_only=True)
'''
both model2 and model3 failed.
The error is:
my_model1 does not appear to have a file named config.json

my_model files:
adapter_config.json, adapter_model.bin , adapter_model.safetensors, README.md, training_args.bin
my_model1 files:
adapter_config.json, adapter_model.safetensors, logs/ README.md

note that removing the adapter_ prefix from the files didn't help and caused the error:
Should have a model_type key in its config.json, or contain one of the following strings in its name: albert, align, altclip, audio-spectrogram-transformer, autoformer, bark, bart, beit, bert, bert-generation, big_bird, bigbird_pegasus, biogpt, bit, blenderbot, blenderbot-small, blip, blip-2, bloom, bridgetower, camembert, canine, chinese_clip, clap, clip, clipseg, codegen, conditional_detr, convbert, convnext, convnextv2, cpmant, ctrl, cvt, data2vec-audio, data2vec-text, data2vec-vision, deberta, deberta-v2, decision_transformer, deformable_detr, deit, deta, detr, dinat, distilbert, donut-swin, dpr, dpt, efficientformer, efficientnet, electra, encodec, encoder-decoder, ernie, ernie_m, esm, falcon, flaubert, flava, fnet, focalnet, fsmt, funnel, git, glpn, gpt-sw3, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gptj, gptsan-japanese, graphormer, groupvit, hubert, ibert, imagegpt, informer, instructblip, jukebox, layoutlm, layoutlmv2, layoutlmv3, led, levit, lilt, llama, longformer, longt5, luke, lxmert, m2m_100, marian, markuplm, mask2former, maskformer, maskformer-swin, mbart, mctct, mega, megatron-bert, mgp-str, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, mpnet, mra, mt5, musicgen, mvp, nat, nezha, nllb-moe, nystromformer, oneformer, open-llama, openai-gpt, opt, owlvit, pegasus, pegasus_x, perceiver, pix2struct, plbart, poolformer, prophetnet, qdqbert, rag, realm, reformer, regnet, re...

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