Instructions to use ifx-pse-sys-ml/flame-27m-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ifx-pse-sys-ml/flame-27m-instruct with Transformers:

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

pipe = pipeline("text-generation", model="ifx-pse-sys-ml/flame-27m-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ifx-pse-sys-ml/flame-27m-instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ifx-pse-sys-ml/flame-27m-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ifx-pse-sys-ml/flame-27m-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ifx-pse-sys-ml/flame-27m-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ifx-pse-sys-ml/flame-27m-instruct

SGLang

How to use ifx-pse-sys-ml/flame-27m-instruct 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 "ifx-pse-sys-ml/flame-27m-instruct" \
    --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": "ifx-pse-sys-ml/flame-27m-instruct",
		"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 "ifx-pse-sys-ml/flame-27m-instruct" \
        --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": "ifx-pse-sys-ml/flame-27m-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ifx-pse-sys-ml/flame-27m-instruct with Docker Model Runner:
```
docker model run hf.co/ifx-pse-sys-ml/flame-27m-instruct
```

flame-27m-instruct / tokenizer_config.json

lyonsn4

Add flame-27m (300B-token base + SmolTalk instruct)

5797e9d verified about 1 month ago

Raw History Blame Contribute Delete

4.92 kB

{
  "add_bos_token": false,
  "add_eos_token": false,
  "add_prefix_space": false,
  "added_tokens_decoder": {
    "0": {
      "content": "<|endoftext|>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    },
    "1": {
      "content": "<|im_start|>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    },
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      "content": "<|im_end|>",
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    },
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    },
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      "special": true
    },
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    "20": {
      "content": "<tts_text_bos_single>",
      "lstrip": false,
      "normalized": false,
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      "single_word": false,
      "special": true
    }
  },
  "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|>",
    "<|audio_start|>",
    "<|audio_end|>",
    "<|audio_pad|>",
    "<tts_pad>",
    "<tts_text_bos>",
    "<tts_text_eod>",
    "<tts_text_bos_single>"
  ],
  "audio_bos_token": "<|audio_start|>",
  "audio_eos_token": "<|audio_end|>",
  "audio_token": "<|audio_pad|>",
  "bos_token": "<|im_start|>",
  "clean_up_tokenization_spaces": false,
  "eos_token": "<|im_end|>",
  "extra_special_tokens": {},
  "image_token": "<|image_pad|>",
  "legacy": true,
  "model_max_length": 131072,
  "pad_token": "<|endoftext|>",
  "sp_model_kwargs": {},
  "spaces_between_special_tokens": false,
  "tokenizer_class": "PreTrainedTokenizerFast",
  "unk_token": "<|endoftext|>",
  "video_token": "<|video_pad|>",
  "vision_bos_token": "<|vision_start|>",
  "vision_eos_token": "<|vision_end|>"
}

	{
	"add_bos_token": false,
	"add_eos_token": false,
	"add_prefix_space": false,
	"added_tokens_decoder": {
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	"<\|object_ref_end\|>",
	"<\|box_start\|>",
	"<\|box_end\|>",
	"<\|quad_start\|>",
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	"<\|vision_pad\|>",
	"<\|image_pad\|>",
	"<\|video_pad\|>",
	"<\|audio_start\|>",
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	"<\|audio_pad\|>",
	"<tts_pad>",
	"<tts_text_bos>",
	"<tts_text_eod>",
	"<tts_text_bos_single>"
	],
	"audio_bos_token": "<\|audio_start\|>",
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	"legacy": true,
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	"pad_token": "<\|endoftext\|>",
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	"spaces_between_special_tokens": false,
	"tokenizer_class": "PreTrainedTokenizerFast",
	"unk_token": "<\|endoftext\|>",
	"video_token": "<\|video_pad\|>",
	"vision_bos_token": "<\|vision_start\|>",
	"vision_eos_token": "<\|vision_end\|>"
	}