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Model: kosiasuzu/telos-agent-llama-3.1-8b-init
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- agent
+license: llama3.1
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B
+pipeline_tag: text-generation
+---
+
+# Telos Llama-3.1-8B (init)
+ 
+A Llama-3.1-8B base model with eleven of its reserved special tokens
+seeded with semantically related-content-token embeddings, in
+preparation for fine-tuning on the [Telos](https://github.com/) agent
+trajectory format.
+ 
+This is **not a fine-tuned agent model.** It is the base model with
+embedding initialization applied. Behavior on any task is identical
+or near-identical to vanilla Llama-3.1-8B-base; the only difference is
+that the eleven Telos reserved tokens now have non-zero embeddings in
+both the input and output matrices.
+ 
+## Model details
+ 
+- **Base model:** `meta-llama/Llama-3.1-8B`
+- **Modification:** in-place initialization of eleven reserved-token rows
+  in `embed_tokens` and `lm_head`
+- **Initialization method:** for each Telos marker, the mean of the
+  input/output embeddings of 2-3 semantically related content tokens
+- **Tokenizer:** unchanged from the base model
+- **Vocabulary size:** unchanged (128 256)
+## Token mapping
+ 
+The Telos format aliases these eleven reserved tokens to frame markers
+at the string level. The tokenizer in this repo is unchanged from the
+base; aliasing is done by the Telos SDK at encode/decode time.
+ 
+| Telos marker     | Reserved token                    | Token ID | Seed words                              |
+| ---------------- | --------------------------------- | -------- | --------------------------------------- |
+| `<\|goal\|>`       | `<\|reserved_special_token_0\|>`    | 128002   | goal, objective, purpose                |
+| `<\|mission\|>`    | `<\|reserved_special_token_1\|>`    | 128003   | mission, task, instruction              |
+| `<\|obs\|>`        | `<\|reserved_special_token_2\|>`    | 128005   | observation, context, environment       |
+| `<\|belief\|>`     | `<\|reserved_special_token_3\|>`    | 128011   | belief, state, knowledge                |
+| `<\|plan\|>`       | `<\|reserved_special_token_4\|>`    | 128012   | plan, strategy, approach                |
+| `<\|think\|>`      | `<\|reserved_special_token_5\|>`    | 128013   | think, reasoning, thought               |
+| `<\|action\|>`     | `<\|reserved_special_token_6\|>`    | 128014   | action, call, tool                      |
+| `<\|end\|>`        | `<\|reserved_special_token_7\|>`    | 128015   | end, stop, done                         |
+| `<\|result\|>`     | `<\|reserved_special_token_8\|>`    | 128016   | result, output, response                |
+| `<\|feedback\|>`   | `<\|reserved_special_token_9\|>`    | 128017   | feedback, update, progress              |
+| `<\|reward\|>`     | `<\|reserved_special_token_10\|>`   | 128018   | reward, score                           |
+ 
+## Why initialization was needed
+ 
+In the base Llama-3.1-8B model, all 250 reserved special tokens have
+**all-zero embeddings** in both `embed_tokens` and `lm_head`. They were
+registered as vocabulary entries but never received any pretraining
+gradient.
+ 
+For Telos, this is degenerate: the model cannot read the markers as
+input (zero embedding contributes nothing) and cannot emit them as
+output (zero `lm_head` row → near-zero logit → near-zero probability
+after softmax). Empirically, prompting the base model with a
+Telos-formatted trajectory causes the model to ignore the markers
+entirely and loop on prose content.
+ 
+Mean-of-related-tokens initialization seeds each marker with a sensible
+starting representation. The model still does not understand the Telos
+format - that requires fine-tuning - but the markers now contribute
+meaningful signal to the forward pass and have non-zero output logits.
+ 
+## Intended use
+ 
+This checkpoint is intended as the starting point for fine-tuning on
+Telos-formatted trajectories. Use it the same way you would use the
+plain Llama-3.1-8B base.
+ 
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+ 
+tokenizer = AutoTokenizer.from_pretrained("kosiasuzu/telos-llama-3.1-8b-init")
+model = AutoModelForCausalLM.from_pretrained(
+    "kosiasuzu/telos-llama-3.1-8b-init",
+    torch_dtype="bfloat16",
+    device_map="auto",
+)
+```
+ 
+## Out-of-scope use
+ 
+- **Not an agent yet.** This checkpoint has not been trained on any
+  agent trajectories. Do not expect it to follow the Telos format
+  correctly.
+- **Not an instruction-tuned model.** It inherits all the base-model
+  limitations of Llama-3.1-8B (looping on greedy decoding, no
+  instruction following).
+- All limitations and biases of Llama-3.1-8B base apply unchanged.
+## License
+ 
+Inherits the Llama 3.1 Community License from the base model. Use of
+this model is subject to that license's terms.
+ 
+## Citation
+ 
+If you build on this, please cite the Telos project and the underlying
+Llama-3.1 model.