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Model: jeongseokoh/LatentSC_llama3.1_8b_6SummaryTokens Source: Original Platform
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README.md
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---
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base_model: meta-llama/Llama-3.1-8B-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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language: en
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---
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# LatentSC Llama 3.1 8B with Summary Tokens
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This repository contains a Llama 3.1 8B Instruct backbone with LatentSC Summary-token embeddings attached. The base model weights are unchanged; only the Summary token embeddings are added so that LatentSC inference can use the trained Summary tokens.
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## Usage
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```python
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import torch
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import torch.nn.functional as F
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from transformers import AutoTokenizer, AutoModelForCausalLM
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repo = "jeongseokoh/LatentSC_llama3.1_8b_6SummaryTokens"
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tokenizer = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(
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repo, torch_dtype=torch.bfloat16, device_map="auto"
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)
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# Summary tokens (default: 6)
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summary_tokens = [f"<|Summary{i}|>" for i in range(1, 7)]
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Solve: 17 * 23. Show the final answer only."},
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False)
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prompt_with_summary = prompt + "".join(summary_tokens)
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inputs = tokenizer(prompt_with_summary, return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(
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**inputs,
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max_new_tokens=128,
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do_sample=True,
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temperature=0.9,
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top_p=0.95,
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num_return_sequences=10,
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pad_token_id=tokenizer.eos_token_id,
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return_dict_in_generate=True,
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output_hidden_states=True,
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)
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# Decode candidates
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sequences = out.sequences
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answers = tokenizer.batch_decode(sequences, skip_special_tokens=True)
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# Embeddings: use last hidden state of the final token per sequence
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last_hs = out.hidden_states[-1][-1] # (batch, seq, hidden)
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seq_lens = inputs["attention_mask"].sum(dim=1) - 1
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idx = torch.arange(last_hs.size(0), device=last_hs.device)
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embs = last_hs[idx, seq_lens, :] # (N, D)
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# LSC selection (cosine similarity)
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embs = F.normalize(embs.float(), p=2, dim=1)
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sim = embs @ embs.T
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sim.fill_diagonal_(0.0)
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avg_sim = sim.mean(dim=1)
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best_idx = int(torch.argmax(avg_sim))
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best_answer = answers[best_idx]
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# Dynamic TopK LSC
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def lsc_topk(embs, answers, k):
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embs = F.normalize(embs.float(), p=2, dim=1)
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sim = embs @ embs.T
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sim.fill_diagonal_(0.0)
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avg_sim = sim.mean(dim=1)
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topk_idx = torch.topk(avg_sim, k=k).indices
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sub = embs[topk_idx]
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sub_sim = sub @ sub.T
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sub_sim.fill_diagonal_(0.0)
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sub_avg = sub_sim.mean(dim=1)
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best_local = int(torch.argmax(sub_avg))
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return answers[int(topk_idx[best_local])], float(sub_avg.max())
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best = None
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best_score = -1e9
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for k in [3, 5, 7]:
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cand, score = lsc_topk(embs, answers, k)
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if score > best_score:
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best_score = score
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best = cand
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```
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### Stored LatentSC config fields
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The following config fields are saved (when present) to guide LatentSC inference:
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```text
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lsc_num_special_tokens
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lsc_special_token_prefix
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lsc_aggr
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lsc_remove_eos
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lsc_temp
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```
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For detailed training/inference scripts and full usage, see the GitHub repository:
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https://github.com/jeongseokO/LatentSC_official
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chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message + builtin tools #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if builtin_tools is defined or tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- "<|python_tag|>" + tool_call.name + ".call(" }}
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{%- for arg_name, arg_val in tool_call.arguments | items %}
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{{- arg_name + '="' + arg_val + '"' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- ")" }}
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{%- else %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{#- This means we're in ipython mode #}
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{{- "<|eom_id|>" }}
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{%- else %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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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": 128000,
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"dtype": "bfloat16",
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"lsc_aggr": "mean",
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"lsc_num_special_tokens": 6,
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"lsc_remove_eos": true,
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"lsc_special_token_prefix": "Summary",
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"lsc_temp": 0.5,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
|
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.6",
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"use_cache": true,
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"vocab_size": 128262
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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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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128009
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],
|
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"temperature": 0.6,
|
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"top_p": 0.9,
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"transformers_version": "4.57.6"
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}
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61
special_tokens_map.json
Normal file
61
special_tokens_map.json
Normal file
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|
||||
{
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||||
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||||
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:8bb6c53d09e2fdb939b60181bc9b0e5daef4f35d76bcb20d9180898c408aec18
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size 17211054
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2119
tokenizer_config.json
Normal file
2119
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user