Instructions to use Jagneshdeveloper/Prakrit1.0-7b-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jagneshdeveloper/Prakrit1.0-7b-small with Transformers:

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

pipe = pipeline("text-generation", model="Jagneshdeveloper/Prakrit1.0-7b-small")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jagneshdeveloper/Prakrit1.0-7b-small")
model = AutoModelForCausalLM.from_pretrained("Jagneshdeveloper/Prakrit1.0-7b-small", 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 Jagneshdeveloper/Prakrit1.0-7b-small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jagneshdeveloper/Prakrit1.0-7b-small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jagneshdeveloper/Prakrit1.0-7b-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jagneshdeveloper/Prakrit1.0-7b-small

SGLang

How to use Jagneshdeveloper/Prakrit1.0-7b-small 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 "Jagneshdeveloper/Prakrit1.0-7b-small" \
    --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": "Jagneshdeveloper/Prakrit1.0-7b-small",
		"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 "Jagneshdeveloper/Prakrit1.0-7b-small" \
        --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": "Jagneshdeveloper/Prakrit1.0-7b-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jagneshdeveloper/Prakrit1.0-7b-small with Docker Model Runner:
```
docker model run hf.co/Jagneshdeveloper/Prakrit1.0-7b-small
```

Prakrit1.0-7b-small / tokenizer_config.json

Jagneshdeveloper

Update tokenizer_config.json

d5ea21b verified about 22 hours ago

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	{
	"add_bos_token": false,
	"add_prefix_space": false,
	"added_tokens_decoder": {
	"151643": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
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	"151644": {
	"content": "<\|im_start\|>",
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	"151650": {
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	"151651": {
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	"151652": {
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	"151653": {
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	"151655": {
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	"151656": {
	"content": "<\|video_pad\|>",
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	"151657": {
	"content": "<tool_call>",
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	"151658": {
	"content": "</tool_call>",
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	},
	"151659": {
	"content": "<\|fim_prefix\|>",
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	"normalized": false,
	"rstrip": false,
	"single_word": false,
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	},
	"151660": {
	"content": "<\|fim_middle\|>",
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	"normalized": false,
	"rstrip": false,
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	},
	"151661": {
	"content": "<\|fim_suffix\|>",
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	"151662": {
	"content": "<\|fim_pad\|>",
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	},
	"151663": {
	"content": "<\|repo_name\|>",
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	},
	"151664": {
	"content": "<\|file_sep\|>",
	"lstrip": false,
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	"single_word": false,
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	}
	},
	"additional_special_tokens": [
	"<\|im_start\|>",
	"<\|im_end\|>",
	"<\|object_ref_start\|>",
	"<\|object_ref_end\|>",
	"<\|box_start\|>",
	"<\|box_end\|>",
	"<\|quad_start\|>",
	"<\|quad_end\|>",
	"<\|vision_start\|>",
	"<\|vision_end\|>",
	"<\|vision_pad\|>",
	"<\|image_pad\|>",
	"<\|video_pad\|>"
	],
	"bos_token": null,
	"chat_template": "{%- if tools -%}{{- \"<\|im_start\|>system\\n\" -}}{%- if messages[\"role\"] == \"system\" -%}{{- messages[\"content\"] -}}{%- else -%}{{- \"You are Prakrit, built on top of the Qwen2.5 architecture by jagnesh-developer. You are a helpful assistant.\" -}}{%- endif -%}{{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" -}}{%- for tool in tools -%}{{- \"\\n\" -}}{{- tool \| tojson -}}{%- endfor -%}{{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><\|im_end\|>\\n\" -}}{%- elif messages[\"role\"] == \"system\" -%}{{- \"<\|im_start\|>system\\n\" + messages[\"content\"] + \"<\|im_end\|>\\n\" -}}{%- else -%}{{- \"<\|im_start\|>system\\nYou are Prakrit, built on top of the Qwen2.5 architecture by jagnesh-developer. You are a helpful assistant.<\|im_end\|>\\n\" -}}{%- endif -%}{%- for message in messages -%}{%- if message[\"role\"] == \"user\" or (message[\"role\"] == \"system\" and not loop.first) or (message[\"role\"] == \"assistant\" and not message[\"tool_calls\"]) -%}{{- \"<\|im_start\|>\" + message[\"role\"] + \"\\n\" + message[\"content\"] + \"<\|im_end\|>\" + \"\\n\" -}}{%- elif message[\"role\"] == \"assistant\" -%}{{- \"<\|im_start\|>\" + message[\"role\"], -}}{%- if message[\"content\"] -%}{{- \"\\n\" + message[\"content\"] -}}{%- endif -%}{%- for tool_call in message[\"tool_calls\"] -%}{%- if tool_call.function is defined -%}{%- set tool_call = tool_call.function -%}{%- endif -%}{{- \"\\n<tool_call>\\n{\\\"name\\\": \\\"\" -}}{{- tool_call.name -}}{{- \"\\\", \\\"arguments\\\": \" -}}{{- tool_call.arguments \| tojson -}}{{- \"\\\"\\n</tool_call>\" -}}{%- endfor -%}{{- \"<\|im_end\|>\\n\" -}}{%- elif message[\"role\"] == \"tool\" -%}{%- if loop.index0 == 0 or messages[loop.index0 - 1 GRAD][\"role\"] != \"tool\" -%}{{- \"<\|im_start\|>user\" -}}{%- endif -%}{{- \"\\n<tool_response>\\n\" -}}{{- message[\"content\"] -}}{{- \"\\n</tool_response>\" -}}{%- if loop.last or messages[loop.index0 + 1][\"role\"] != \"tool\" -%}{{- \"<\|im_end\|>\\n\" -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- \"<\|im_start\|>assistant\\n\" -}}{%- endif -%}",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|im_end\|>",
	"errors": "replace",
	"model_max_length": 32768,
	"pad_token": "<\|endoftext\|>",
	"split_special_tokens": false,
	"tokenizer_class": "Qwen2Tokenizer",
	"unk_token": null
	}