Instructions to use d4rkninja/tanpo-fundraising with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d4rkninja/tanpo-fundraising with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="d4rkninja/tanpo-fundraising") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("d4rkninja/tanpo-fundraising") model = AutoModelForCausalLM.from_pretrained("d4rkninja/tanpo-fundraising", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use d4rkninja/tanpo-fundraising with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "d4rkninja/tanpo-fundraising" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "d4rkninja/tanpo-fundraising", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/d4rkninja/tanpo-fundraising
- SGLang
How to use d4rkninja/tanpo-fundraising 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 "d4rkninja/tanpo-fundraising" \ --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": "d4rkninja/tanpo-fundraising", "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 "d4rkninja/tanpo-fundraising" \ --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": "d4rkninja/tanpo-fundraising", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use d4rkninja/tanpo-fundraising with Docker Model Runner:
docker model run hf.co/d4rkninja/tanpo-fundraising
Replace in place: tanpo-fundraising merged (PEFT, no Unsloth)
Browse files- README.md +23 -0
- chat_template.jinja +45 -0
- config.json +61 -0
- generation_config.json +7 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +21 -0
README.md
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---
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base_model: unsloth/LFM2.5-1.2B-Instruct
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library_name: transformers
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license: other
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pipeline_tag: text-generation
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tags:
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- product
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- transformers
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- peft
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- tanpo
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- darklab
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---
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# tanpo-fundraising
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**Owner:** DarkNinja Solutions
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**Creator:** d4rkninja
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**Base model:** [unsloth/LFM2.5-1.2B-Instruct](https://huggingface.co/unsloth/LFM2.5-1.2B-Instruct)
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**Card updated:** 2026-09-18 UTC
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Merged product specialist from LoRA via PEFT `merge_and_unload`. **No v2 label.**
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Export pipeline: transformers + peft merge (no Unsloth) → llama.cpp GGUF.
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chat_template.jinja
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{{- bos_token -}}
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{%- set keep_past_thinking = keep_past_thinking | default(false) -%}
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{%- set ns = namespace(system_prompt="") -%}
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{%- if messages[0]["role"] == "system" -%}
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{%- set ns.system_prompt = messages[0]["content"] -%}
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{%- set messages = messages[1:] -%}
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{%- endif -%}
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{%- if tools -%}
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{%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
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{%- for tool in tools -%}
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{%- if tool is not string -%}
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{%- set tool = tool | tojson -%}
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{%- endif -%}
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{%- set ns.system_prompt = ns.system_prompt + tool -%}
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{%- if not loop.last -%}
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{%- set ns.system_prompt = ns.system_prompt + ", " -%}
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{%- endif -%}
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{%- endfor -%}
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{%- set ns.system_prompt = ns.system_prompt + "]" -%}
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{%- endif -%}
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{%- if ns.system_prompt -%}
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{{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
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{%- endif -%}
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{%- set ns.last_assistant_index = -1 -%}
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{%- for message in messages -%}
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{%- if message["role"] == "assistant" -%}
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{%- set ns.last_assistant_index = loop.index0 -%}
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{%- endif -%}
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{%- endfor -%}
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{%- for message in messages -%}
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{{- "<|im_start|>" + message["role"] + "\n" -}}
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{%- set content = message["content"] -%}
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{%- if content is not string -%}
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{%- set content = content | tojson -%}
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{%- endif -%}
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{%- if message["role"] == "assistant" and not keep_past_thinking and loop.index0 != ns.last_assistant_index -%}
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{%- if "</think>" in content -%}
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{%- set content = content.split("</think>")[-1] | trim -%}
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{%- endif -%}
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{%- endif -%}
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{{- content + "<|im_end|>\n" -}}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{- "<|im_start|>assistant\n" -}}
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{%- endif -%}
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config.json
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{
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"architectures": [
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"Lfm2ForCausalLM"
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],
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"block_auto_adjust_ff_dim": true,
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"block_dim": 2048,
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"block_ffn_dim_multiplier": 1.0,
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"block_mlp_init_scale": 1.0,
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"block_multiple_of": 256,
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"block_norm_eps": 1e-05,
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"block_out_init_scale": 1.0,
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"block_use_swiglu": true,
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"block_use_xavier_init": true,
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"bos_token_id": 1,
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"conv_L_cache": 3,
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"conv_bias": false,
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"conv_dim": 2048,
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"conv_use_xavier_init": true,
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"dtype": "bfloat16",
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"eos_token_id": 7,
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"full_attn_idxs": null,
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"layer_types": [
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"full_attention",
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"conv",
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"full_attention",
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"conv",
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"full_attention",
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| 41 |
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"conv"
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],
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| 43 |
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"max_position_embeddings": 128000,
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"model_type": "lfm2",
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"norm_eps": 1e-05,
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"num_attention_heads": 32,
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"num_heads": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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| 55 |
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"tie_word_embeddings": true,
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| 56 |
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"transformers_version": "5.5.0",
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| 57 |
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"unsloth_fixed": true,
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| 58 |
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"use_cache": true,
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| 59 |
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"use_pos_enc": true,
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"vocab_size": 65536
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 7,
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| 5 |
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"pad_token_id": 0,
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"transformers_version": "5.5.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5650575a2084747e6deebe86252e43ae107cd1bda0ee6303bb3d0d26cee47126
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size 2340697936
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
ADDED
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{
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"backend": "tokenizers",
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| 3 |
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"bos_token": "<|startoftext|>",
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| 4 |
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"clean_up_tokenization_spaces": false,
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| 5 |
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"eos_token": "<|im_end|>",
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| 6 |
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"is_local": true,
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| 7 |
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"legacy": false,
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| 8 |
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"model_input_names": [
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| 9 |
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"input_ids",
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| 10 |
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"attention_mask"
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| 11 |
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],
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| 12 |
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"model_max_length": 128000,
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| 13 |
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"pad_token": "<|pad|>",
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| 14 |
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"padding_side": "left",
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| 15 |
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"sp_model_kwargs": {},
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| 16 |
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"spaces_between_special_tokens": false,
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| 17 |
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"tokenizer_class": "TokenizersBackend",
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| 18 |
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"unk_token": null,
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| 19 |
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"use_default_system_prompt": false,
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| 20 |
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"use_fast": true
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}
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