Instructions to use VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16")
model = AutoModelForCausalLM.from_pretrained("VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16

SGLang

How to use VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16 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 "VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16" \
    --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": "VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16",
		"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 "VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16" \
        --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": "VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16 with Docker Model Runner:
```
docker model run hf.co/VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16
```

Mistral-7B-Instruct-v0.3 text-to-SQL, bf16

mistralai/Mistral-7B-Instruct-v0.3 fine-tuned on text-to-SQL, merged and left in bf16. It is the ceiling arm of a panel of 7 arms: every quantized arm below was made from this checkpoint and allocated the same byte budget, so their accuracies differ by method and not by size.

What this is


base model	mistralai/Mistral-7B-Instruct-v0.3
fine-tune	lora r=32, 2.0 epoch over 39,531 text-to-SQL conversations
training data	`gretelai/synthetic_text_to_sql`, `Salesforce/wikisql`, `b-mc2/sql-create-context`
quantization	none -- this is the bf16 fine-tune every quantized arm was made from
size on disk	13.500 GiB (16.0000 bits per parameter)
loads with	`transformers`

Results

Execution match on 2,454 held-out text-to-SQL problems: the generated query is run against the schema and compared to the reference result set.

arm	exec match	size	bits/param
bf16	78.16%	13.500 GiB	16.0000
gptq_4b	78.28%	3.692 GiB	4.3760
awq_4b	77.91%	3.692 GiB	4.3760
dq_4b	78.08%	3.692 GiB	4.3754
gptq_3b	6.68%	2.858 GiB	3.3869
awq_3b	74.16%	2.858 GiB	3.3869
dq_3b	75.22%	2.857 GiB	3.3859

This arm, by evaluation source:

eval source	exec match	items
`gretel`	77.02%	818
`spider`	63.57%	818
`wikisql`	93.89%	818

How this arm compares

McNemar exact over the per-item hits, so every row is a paired test on the same problems in the same order. p (Holm) is step-down corrected within the family the panel declared, not within this card.

comparison	delta (pts)	95% CI	p	p (Holm)	verdict
4b GPTQ vs bf16	+0.12	[-0.54, +0.79]	0.81	1	not separated
4b AWQ vs bf16	-0.24	[-1.05, +0.56]	0.621	1	not separated
4b DynQuant vs bf16	-0.08	[-0.83, +0.67]	0.915	1	not separated
3b GPTQ vs bf16	-71.48	[-73.30, -69.65]	0	0	separated
3b AWQ vs bf16	-3.99	[-5.29, -2.70]	1.8e-09	9.01e-09	separated
3b DynQuant vs bf16	-2.93	[-4.16, -1.71]	3.55e-06	1.42e-05	separated

What is not claimed

The baselines run at their own libraries' defaults, and those defaults are not the same scheme. GPTQ here is symmetric with no activation reordering; AWQ and DynQuant are asymmetric. Where a comparison above pairs a symmetric arm against an asymmetric one its delta spans two differences at once -- how the bits were allocated, and whether a zero point was stored per group -- so a large gap between those two arms is not on its own evidence about allocation. The comparison that would isolate it, two arms of the same scheme at the same byte anchor, is not in this panel.
Storage, measured; throughput, not. The number reported here is bytes on disk and execution match. This card makes no claim about decode speed or peak VRAM against an fp16 baseline, because this panel did not measure either.
One task. Execution match on held-out text-to-SQL is what was scored. It says nothing about how this arm behaves on anything else, and a quantization that holds one task can lose another.

Install

This checkpoint is plain bf16 and loads with transformers alone. It is the ceiling arm of a DynQuant panel: the quantized arms in the table above are this same fine-tune at a fraction of the size, and those need the package.

pip install dynquant

Source, format spec, and the allocator that produced their bit maps: https://github.com/kambojvikram/dynquant

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16", device_map="cuda")
tokenizer = AutoTokenizer.from_pretrained("VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16")

Provenance

panel model: /workspace/runs/s4/mistral7b-v03.text2sql/merged
parameters counted: 7,248,023,552
byte target: --
fine-tune: 2472 steps, train loss 0.0540, 3.8 h
fine-tune commit: 5959fe04b7db0512eaf7567d77a43f3df5860651
evaluation: 2,454 problems in 10 min

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for VikramPal/mistral-7b-instruct-v0.3-text2sql-bf16

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Finetuned

(532)

this model