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Model: saadxsalman/SS-Talk-2-Bash Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: peft
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pipeline_tag: text-generation
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base_model: LiquidAI/LFM2.5-350M
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tags:
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- bash
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- terminal
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- devops
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- linux
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- hardcoded
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- lfm
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datasets:
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- emirkaanozdemr/bash_command_data_6K
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model_creator: saadxsalman
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widget:
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- text: "[NL] find all files larger than 100MB in the current directory [CL]"
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example_title: "Find Large Files"
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- text: "[NL] list all files in long format including hidden ones [CL]"
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example_title: "List All Files"
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inference:
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parameters:
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do_sample: false
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temperature: 0.0
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max_new_tokens: 64
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---
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# SS-Talk-2-Bash (LFM-350M-Hardcoded)
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This model is a fine-tuned version of **LiquidAI/LFM2.5-350M** designed specifically for deterministic natural language to Bash command translation. It uses a **Strict Hard-Coding** training method to minimize linguistic "chatter" and maximize structural accuracy.
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---
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## 1. Model Description
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* **Developed by:** saadxsalman
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* **Model type:** Causal Language Model (LFM)
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* **Language(s):** English (Input) to Bash (Output)
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* **License:** Apache 2.0
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* **Finetuned from model:** LiquidAI/LFM2.5-350M
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---
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## 2. Training Strategy: "The Hard-Coding Engine"
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Unlike standard instruction-tuned models that learn to be "helpful assistants," this model was trained using a **Masking Collator** strategy:
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* **Label Masking:** All natural language tokens (the prompt) are masked during training ($loss = -100$). The model only calculates loss on the Bash command itself.
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* **Zero Chatter:** The model does not learn to say "Sure, here is your command." It is trained to jump directly from the `[CL]` token to the syntax.
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* **Greedy Decoding:** The `generation_config.json` is locked to `do_sample: False` and `temperature: 0.0` to ensure the same input always produces the same output.
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---
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## 3. Training Data
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The model was fine-tuned on the `emirkaanozdemr/bash_command_data_6K` dataset. The data was restructured into a rigid non-linguistic format:
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`[NL] {Natural Language Prompt} [CL] {Bash Command} [END]`
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---
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## 4. Intended Use & Prompting
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To get the best results, you **must** use the specific trigger tokens used during training.
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**Correct Prompt Format:**
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`[NL] find all files larger than 100MB in the current directory [CL]`
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---
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## 5. How to Use (Inference)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "saadxsalman/SS-Talk-2-Bash"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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prompt = "[NL] list all files in long format [CL]"
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=64)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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---
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## 6. Limitations and Biases
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* **Logic Only:** This model has "forgotten" how to converse. It will not answer general questions or write Python code.
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* **Bash Specific:** It is optimized for standard Linux Bash commands. It may struggle with complex shell scripting logic if not represented in the 6K training samples.
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* **Formatting Sensitive:** If the `[NL]` or `[CL]` tokens are omitted, the model performance will degrade significantly.
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---
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## 7. Training Hyperparameters
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| Parameter | Value |
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| :--- | :--- |
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| **Learning Rate** | $1 \times 10^{-4}$ |
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| **Optimizer** | Paged AdamW 8-bit |
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| **LoRA R** | 64 |
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| **LoRA Alpha** | 128 |
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| **Batch Size** | 16 (4 per device $\times$ 4 grad accum) |
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| **Precision** | Mixed Precision (FP16) |
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```
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64
chat_template.jinja
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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 sys_content = messages[0]["content"] -%}
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{%- if sys_content is not string -%}
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{%- for item in sys_content -%}
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{%- if item["type"] == "text" -%}
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{%- set ns.system_prompt = ns.system_prompt + item["text"] -%}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{%- set ns.system_prompt = sys_content -%}
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{%- endif -%}
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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 ns.content = "" -%}
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{%- for item in content -%}
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{%- if item["type"] == "image" -%}
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{%- set ns.content = ns.content + "<image>" -%}
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{%- elif item["type"] == "text" -%}
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{%- set ns.content = ns.content + item["text"] -%}
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{%- else -%}
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{%- set ns.content = ns.content + item | tojson -%}
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{%- endif -%}
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{%- endfor -%}
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{%- set content = ns.content -%}
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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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61
config.json
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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": 1024,
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"block_ff_dim": 6656,
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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": 1024,
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"conv_use_xavier_init": true,
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"dtype": "float16",
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"eos_token_id": 7,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 6656,
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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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"conv"
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],
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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": 16,
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"num_heads": 16,
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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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"tie_embedding": true,
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"use_cache": true,
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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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"do_sample": false,
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"temperature": 0.0,
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"top_p": 1.0,
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"num_beams": 1,
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"max_new_tokens": 128,
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"eos_token_id": 7,
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"pad_token_id": 0,
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"transformers_version": "4.38.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7148cb24393b2529de6fe6516bba8d8727c79a88a72aef82b06df7e1a6174b18
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size 708984312
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3
ss-talk-2-bash.Q8_0.gguf
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3
ss-talk-2-bash.Q8_0.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:8d546aa98cfb1750d056c374f4373670e59a901a83aa82935f91e96639f5cc91
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size 379217056
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323830
tokenizer.json
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323830
tokenizer.json
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tokenizer_config.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<|startoftext|>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"extra_special_tokens": [],
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"is_local": false,
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"legacy": false,
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 1000000000000000019884624838656,
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"model_specific_special_tokens": {},
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"pad_token": "<|pad|>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "TokenizersBackend",
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"use_default_system_prompt": false,
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"use_fast": true
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}
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