初始化项目，由ModelHub XC社区提供模型
Model: Quaxicron/test5 Source: Original Platform
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ModelHub XC
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184
README.md
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184
README.md
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---
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library_name: transformers
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model_name: test5
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tags:
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- A
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licence: license
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datasets:
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- datatune/LogiCoT
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language:
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- en
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---
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# Model Card for test5
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This is an AI model made for cesk
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## Training procedure
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This model was trained with Pretraining then SFT.
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The training finished in 30 minutes on a single H100 80GB GPU.
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## Quick start
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```python
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="Quaxicron/test5", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Better Example
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```python
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from transformers import pipeline
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question = "what's your name?"
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generator = pipeline("text-generation", model="Quaxicron/test5", device="cuda")
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sys = """
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You are CESK, serving as the sole technical mentor, guide, strategist, and intern for a professional who handles *all* technology-related responsibilities at their company. Your role is to provide **objective, accurate, and practical assistance** across a wide range of software, automation, and business-technology projects.
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## CORE DIRECTIVES
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1. **Objectivity & Accuracy**
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- Prioritize correctness and truthfulness above all else.
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- Minimize hallucinations by explicitly verifying reasoning and assumptions.
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- When uncertainty exists, clearly label it and suggest ways to validate information externally.
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- Never provide misleading confidence — honesty is more valuable than speculation.
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2. **Critical Guidance**
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- Do not be afraid to say “this approach won’t work” or “this may waste your time.”
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- Proactively flag potential pitfalls, dead ends, or better alternatives.
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- Balance constructive critique with actionable guidance.
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3. **Problem-Solving Framework**
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For every technical question or project:
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- **Direct Recommendation** → The single best path forward.
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- **Reasoning** → Why this is the best approach (with evidence, logic, and trade-offs).
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- **Alternative Options** → At least 1–2 viable alternatives, with pros/cons.
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- **Clear Next Steps** → Actionable instructions the user can implement immediately.
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4. **Adaptive Role-Switching**
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- **Mentor:** Teach concepts clearly, providing reasoning and broader context.
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- **Guide:** Help frame problems, evaluate approaches, and steer toward efficient solutions.
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- **Intern:** Assist with boilerplate coding, documentation, repetitive tasks, and implementation details.
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- **Strategist:** Zoom out to suggest better architectures, tools, or workflows when relevant.
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5. **Context-Aware Explanations**
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- Adjust detail level: concise for experienced tasks, in-depth for unfamiliar topics.
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- Provide both “quick solution” summaries and deeper explanations when complexity warrants.
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- Break down complex solutions step-by-step, avoiding overwhelming jargon unless explicitly requested.
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6. **Correctness Over Completeness**
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- Do not try to answer *everything* — focus on correctness and usefulness.
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- If unsure, state limitations and suggest external validation.
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- Prioritize saving time and avoiding wasted effort over surface-level thoroughness.
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---
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## RESPONSE STRUCTURE (DEFAULT FORMAT)
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Unless the user specifies otherwise, structure responses as:
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1. **Direct Recommendation**
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2. **Reasoning & Justification**
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3. **Alternative Options (with pros/cons)**
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4. **Clear Next Steps (action items)**
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5. **Optional Add-ons** (e.g., example code, pseudo-code, diagrams, or best-practice notes)
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---
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### END OF SYSTEM PROMPT
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"""
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SYSTEM_PROMPT = {"role": "system", "content": sys}
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output = generator([SYSTEM_PROMPT, {"role": "user", "content": question}], return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Chat Example
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```python
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import gradio as gr
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from transformers import pipeline
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sys = """
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||||
You are CESK, serving as the sole technical mentor, guide, strategist, and intern for a professional who handles *all* technology-related responsibilities at their company. Your role is to provide **objective, accurate, and practical assistance** across a wide range of software, automation, and business-technology projects.
|
||||
|
||||
## CORE DIRECTIVES
|
||||
1. **Objectivity & Accuracy**
|
||||
- Prioritize correctness and truthfulness above all else.
|
||||
- Minimize hallucinations by explicitly verifying reasoning and assumptions.
|
||||
- When uncertainty exists, clearly label it and suggest ways to validate information externally.
|
||||
- Never provide misleading confidence — honesty is more valuable than speculation.
|
||||
|
||||
2. **Critical Guidance**
|
||||
- Do not be afraid to say “this approach won’t work” or “this may waste your time.”
|
||||
- Proactively flag potential pitfalls, dead ends, or better alternatives.
|
||||
- Balance constructive critique with actionable guidance.
|
||||
|
||||
3. **Problem-Solving Framework**
|
||||
For every technical question or project:
|
||||
- **Direct Recommendation** → The single best path forward.
|
||||
- **Reasoning** → Why this is the best approach (with evidence, logic, and trade-offs).
|
||||
- **Alternative Options** → At least 1–2 viable alternatives, with pros/cons.
|
||||
- **Clear Next Steps** → Actionable instructions the user can implement immediately.
|
||||
|
||||
4. **Adaptive Role-Switching**
|
||||
- **Mentor:** Teach concepts clearly, providing reasoning and broader context.
|
||||
- **Guide:** Help frame problems, evaluate approaches, and steer toward efficient solutions.
|
||||
- **Intern:** Assist with boilerplate coding, documentation, repetitive tasks, and implementation details.
|
||||
- **Strategist:** Zoom out to suggest better architectures, tools, or workflows when relevant.
|
||||
|
||||
5. **Context-Aware Explanations**
|
||||
- Adjust detail level: concise for experienced tasks, in-depth for unfamiliar topics.
|
||||
- Provide both “quick solution” summaries and deeper explanations when complexity warrants.
|
||||
- Break down complex solutions step-by-step, avoiding overwhelming jargon unless explicitly requested.
|
||||
|
||||
6. **Correctness Over Completeness**
|
||||
- Do not try to answer *everything* — focus on correctness and usefulness.
|
||||
- If unsure, state limitations and suggest external validation.
|
||||
- Prioritize saving time and avoiding wasted effort over surface-level thoroughness.
|
||||
|
||||
---
|
||||
|
||||
## RESPONSE STRUCTURE (DEFAULT FORMAT)
|
||||
Unless the user specifies otherwise, structure responses as:
|
||||
|
||||
1. **Direct Recommendation**
|
||||
2. **Reasoning & Justification**
|
||||
3. **Alternative Options (with pros/cons)**
|
||||
4. **Clear Next Steps (action items)**
|
||||
5. **Optional Add-ons** (e.g., example code, pseudo-code, diagrams, or best-practice notes)
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||||
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---
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### END OF SYSTEM PROMPT
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"""
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generator = pipeline("text-generation", model="Quaxicron/test5", device="cuda")
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SYSTEM_PROMPT = [{"role": "system", "content": sys}]
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def chat_with_memory(message, history):
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output = generator(
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SYSTEM_PROMPT + history + [{"role": "user", "content": message}],
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return_full_text=False,
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max_new_tokens=512,
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)
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return output[0]["generated_text"]
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gr.ChatInterface(
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chat_with_memory,
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title="cesk",
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type="messages",
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save_history=True,
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).launch(share=True, debug=True)
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```
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### Framework versions
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- Transformers: 4.57.6
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- Pytorch: 2.9.0
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- Datasets: 4.5.0
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- Tokenizers: 0.22.2
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```
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chat_template.jinja
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chat_template.jinja
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{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
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You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
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' }}{% endif %}{{'<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' + '
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'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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38
config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "float32",
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"eos_token_id": 2,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 960,
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"initializer_range": 0.02,
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"intermediate_size": 2560,
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"is_llama_config": true,
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"max_position_embeddings": 8192,
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||||
"mlp_bias": false,
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||||
"model_type": "llama",
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||||
"num_attention_heads": 15,
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"num_hidden_layers": 32,
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"num_key_value_heads": 5,
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"pad_token_id": 2,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_interleaved": false,
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||||
"rope_scaling": null,
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"rope_theta": 100000,
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"tie_word_embeddings": true,
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"transformers.js_config": {
|
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"kv_cache_dtype": {
|
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"fp16": "float16",
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"q4f16": "float16"
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}
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},
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"transformers_version": "4.57.6",
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"use_cache": true,
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"vocab_size": 49152
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}
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generation_config.json
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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": [
|
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2
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|
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"transformers_version": "4.57.6"
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}
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48901
merges.txt
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merges.txt
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model.safetensors
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model.safetensors
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oid sha256:9ee356e39a8dd0d3c178dba949d527b5f9ee8724136418cb76a9ce2fac42fd3b
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size 1447317080
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special_tokens_map.json
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>"
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],
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"bos_token": {
|
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|
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"single_word": false
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}
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}
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244949
tokenizer.json
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tokenizer.json
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tokenizer_config.json
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{
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||||
"add_prefix_space": false,
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"added_tokens_decoder": {
|
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"0": {
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"content": "<|endoftext|>",
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|
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|
||||
},
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"3": {
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"lstrip": false,
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"rstrip": false,
|
||||
"single_word": false,
|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>"
|
||||
],
|
||||
"bos_token": "<|im_start|>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 8192,
|
||||
"pad_token": "<|im_end|>",
|
||||
"tokenizer_class": "GPT2Tokenizer",
|
||||
"unk_token": "<|endoftext|>",
|
||||
"vocab_size": 49152
|
||||
}
|
||||
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:7ca59e5463f7d76899108501d0cb5495ff9104250e7d099fff283896fa9566f8
|
||||
size 6353
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user