[Docs] Reduce custom syntax used in docs (#27009)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

docs/features/spec_decode.md

@@ -3,7 +3,7 @@
 !!! warning
     Please note that speculative decoding in vLLM is not yet optimized and does
     not usually yield inter-token latency reductions for all prompt datasets or sampling parameters.
-    The work to optimize it is ongoing and can be followed here: <gh-issue:4630>
+    The work to optimize it is ongoing and can be followed here: <https://github.com/vllm-project/vllm/issues/4630>
 
 !!! warning
     Currently, speculative decoding in vLLM is not compatible with pipeline parallelism.
@@ -183,7 +183,7 @@ A variety of speculative models of this type are available on HF hub:
 ## Speculating using EAGLE based draft models
 
 The following code configures vLLM to use speculative decoding where proposals are generated by
-an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https://arxiv.org/pdf/2401.15077) based draft model. A more detailed example for offline mode, including how to extract request level acceptance rate, can be found [here](gh-file:examples/offline_inference/eagle.py).
+an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https://arxiv.org/pdf/2401.15077) based draft model. A more detailed example for offline mode, including how to extract request level acceptance rate, can be found [here](../../examples/offline_inference/spec_decode.py).
 
 ??? code
 
@@ -218,8 +218,8 @@ an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https
 A few important things to consider when using the EAGLE based draft models:
 
 1. The EAGLE draft models available in the [HF repository for EAGLE models](https://huggingface.co/yuhuili) should
-   be able to be loaded and used directly by vLLM after <gh-pr:12304>.
-   If you are using vllm version before <gh-pr:12304>, please use the
+   be able to be loaded and used directly by vLLM after <https://github.com/vllm-project/vllm/pull/12304>.
+   If you are using vllm version before <https://github.com/vllm-project/vllm/pull/12304>, please use the
    [script](https://gist.github.com/abhigoyal1997/1e7a4109ccb7704fbc67f625e86b2d6d) to convert the speculative model,
    and specify `"model": "path/to/modified/eagle/model"` in `speculative_config`. If weight-loading problems still occur when using the latest version of vLLM, please leave a comment or raise an issue.
 
@@ -229,7 +229,7 @@ A few important things to consider when using the EAGLE based draft models:
 
 3. When using EAGLE-based speculators with vLLM, the observed speedup is lower than what is
    reported in the reference implementation [here](https://github.com/SafeAILab/EAGLE). This issue is under
-   investigation and tracked here: <gh-issue:9565>.
+   investigation and tracked here: <https://github.com/vllm-project/vllm/issues/9565>.
 
 4. When using EAGLE-3 based draft model, option "method" must be set to "eagle3".
    That is, to specify `"method": "eagle3"` in `speculative_config`.
@@ -267,7 +267,7 @@ speculative decoding, breaking down the guarantees into three key areas:
     >   distribution. [View Test Code](https://github.com/vllm-project/vllm/blob/47b65a550866c7ffbd076ecb74106714838ce7da/tests/samplers/test_rejection_sampler.py#L252)
     > - **Greedy Sampling Equality**: Confirms that greedy sampling with speculative decoding matches greedy sampling
     >   without it. This verifies that vLLM's speculative decoding framework, when integrated with the vLLM forward pass and the vLLM rejection sampler,
-    >   provides a lossless guarantee. Almost all of the tests in <gh-dir:tests/spec_decode/e2e>.
+    >   provides a lossless guarantee. Almost all of the tests in [tests/spec_decode/e2e](../../tests/spec_decode/e2e).
     >   verify this property using [this assertion implementation](https://github.com/vllm-project/vllm/blob/b67ae00cdbbe1a58ffc8ff170f0c8d79044a684a/tests/spec_decode/e2e/conftest.py#L291)
 
 3. **vLLM Logprob Stability**
@@ -289,4 +289,4 @@ For mitigation strategies, please refer to the FAQ entry *Can the output of a pr
 - [A Hacker's Guide to Speculative Decoding in vLLM](https://www.youtube.com/watch?v=9wNAgpX6z_4)
 - [What is Lookahead Scheduling in vLLM?](https://docs.google.com/document/d/1Z9TvqzzBPnh5WHcRwjvK2UEeFeq5zMZb5mFE8jR0HCs/edit#heading=h.1fjfb0donq5a)
 - [Information on batch expansion](https://docs.google.com/document/d/1T-JaS2T1NRfdP51qzqpyakoCXxSXTtORppiwaj5asxA/edit#heading=h.kk7dq05lc6q8)
-- [Dynamic speculative decoding](gh-issue:4565)
+- [Dynamic speculative decoding](https://github.com/vllm-project/vllm/issues/4565)